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Strategic Product Displays Across Different Assortment Levels


Online retailers often display products across various assortment sizes, yet how to strategically present products in this context remains unclear. This research addresses this problem by empirically analyzing consumers’ information processing strategies on product displays across different assortment levels. We record consumers’ moment-to-moment attention shift and resort to a non-homogeneous hidden Markov model to identify latent information processing strategies that direct the eye movement. Our results show that the latent heuristic and systematic processing strategies can be identified with three unique attention patterns: preference for visual versus verbal information, sampling inertia, and range of inspection. Furthermore, the assortment levels significantly affect the usage proportion and switching of two processing strategies. Lastly, we reveal the strong predictive power of identified processing strategies on product choice. Based on model findings, we conducted a follow-up experiment to demonstrate how firms can influence product choices by strategically displaying products across assortment levels. We contribute to the growing body of literature on heuristic-systematic information processing and constructive decision-making. Firms can use these findings to decipher information processing strategies based on observed attention patterns and to improve the prediction of product choice. More importantly, the results shed light on the adaptive product display as assortment size varies, and offer insights into customized and dynamic product presentation based on attention shift. Our study also addresses the importance of the “anchor product” design when the assortment is large.

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  1. 1.

    Participants indicated relatively high familiarity with the brands chosen (mean: 4.43, median: 4.69; 1 means not familiar with the brands at all and 7 means very familiar with the brands).

  2. 2.

    We select 5 products (10 AOIs) to represent low assortment and 15 products (30 AOIs) to represent high assortment, based on the current literature. For instance, in Iyengar and Lepper (2000), 6 options were set to be low assortment, and 24 were set to be high assortment. In [48], 8 and 27 products were selected to represent high- vs. low-assortment levels.

  3. 3.

    Formulated as (\({\text{InspectionProb}}_{i,j,t} ({\text{GazeDura}}_{i,j,t} )\sim \sigma_{0} + \sigma_{1} FixSequence_{{i,1,..T_{i} }}\)). (Ti is the total number of fixations of participant i.) We use the estimated parameter ơ1 in the linear regression model between predicted inspection probabilities/gaze duration and the fixation sequence. We also tested the correlation between the predicted fixation probability and duration during Bayesian procedures, based on the estimated parameters at 1,000 intervals after the burn-in period (5,000 draws). The absolute correlation value is 0.592, which does not pose concerns for multicollinearity.


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This research is supported by Santa Clara University Research Funding.

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Correspondence to Savannah Wei Shi.

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Table 5 Theoretical predictions for the effects of variables used in the model
Fig. 6

Illustration of estimated strategy adoption over the course of inspection

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Shi, S.W., Che, H. & Jin, L. Strategic Product Displays Across Different Assortment Levels. Cust. Need. and Solut. 8, 84–101 (2021).

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  • Strategic product display
  • Assortment
  • Heuristic-systematic information processing
  • Constructive decision-making
  • Eye-tracking